---
title: "**COVID19 - New Zealand**"
author: "**Matt Bixley**"
output:
flexdashboard::flex_dashboard:
orientation: rows
source_code: embed
vertical_layout: fill
theme: bootstrap
#logo: images/coronavirus.png
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(ggthemes)
library(scales)
#theme_set(theme_bw)
#------------------ Parameters ------------------
# cases today
nzcases = 0
nzdeaths = 0
#------------------ Data ------------------
source("code/import_jhu.R")
source("code/insert_today.R")
#source("code/population.R")
# read the jhu set for the lat/long
jhu_confirmed <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
jhu <- read_csv(jhu_confirmed) %>%
rename(country = "Country/Region") %>%
map_df(str_replace_all, pattern = " ", replacement = "_") %>%
mutate(country = ifelse(country == "Taiwan*", "Taiwan",country)) %>%
mutate(country = ifelse(country == "Korea,_South", "South_Korea",country)) %>%
mutate(country = ifelse(country == "US", "United_States_of_America",country)) %>%
group_by(country) %>% # hack to get just one copy of country
mutate(first = rank(country)) %>%
filter(first == 1) %>%
select(country, Lat, Long)
# Import from the European CDC
ecdc_url <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/"
download.file(url = ecdc_url, destfile = "data/ecdc.csv")
covid19 <- read_csv("data/ecdc.csv") %>%
rename(date = "dateRep", country = "countriesAndTerritories", population = "popData2018") %>%
left_join(.,jhu) %>% # merge with jhu to get the lat long
mutate(date = dmy(date)) %>%
arrange(date, country) %>%
mutate(population = ifelse(country == "China", 58500000/0.83, population)) %>% #correct pop to reflect hubei
group_by(country) %>%
mutate(cum_cases = cumsum(cases)) %>%
mutate(cum_deaths = cumsum(deaths)) %>%
ungroup() %>%
mutate(country = ifelse(country == "United_States_of_America", "USA",country)) %>% # per capita
filter(country != "Cases_on_an_international_conveyance_Japan")
#covid19 <- insert_today(cases = nzcases, deaths = nzdeaths)
covid19 <- covid19 %>%
mutate(per_million = round(cum_cases/population*1000000,1))
latest <- covid19 %>% filter(date == max(date))
nz <- covid19 %>% filter(country == "New_Zealand")
usa <- covid19 %>% filter(country == "USA")
italy <- covid19 %>% filter(country == "Italy")
```
Sidebar {.sidebar}
=====================================
__**The Lockdown Diaries**__
Updates likely 9am NZDT with the over night European data. Just after 1pm NZDT with the New Zealand daily news and sometime in the evening.
{ width=100% }
**Saturday March 28th** (d3)
**Friday March 27th** (d2)
Got this dashboard underway
3x1min Boats, 2x1min Russian Twists
2x1min Supermans each pair
**Thursday March 26th** (d1)
Home D - loving it
PE instituted for the kids (Not Happy)
3x20 Pushups
3x10 Squat Jumps
30 minute run
**Wednesday March 25th** (d-1)
Mitre10 for Paint rollers and bits and pieces
NZDataScience Zoom Morning Tea
Builders cleaned the windows
Sparky rigged up the Hotwater Cylinder in the extension (good bastard)
3x1min Boats
2x1min Russian Twists
3x10 Forward Lunges
30 minute run
**Tuesday March 24th** (d-2)
Set up the home office, nipped down to work for a chair
5x1min Planks
3x10 Reverse Lunges
30 minute run
**Summary**
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
#valueBox(value, subtitle, icon = NULL, color = "aqua", width = 4,href = NULL)
valueBox(
value = paste(format(max(nz$cum_cases), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = "silver"
)
```
### active {.value-box}
```{r}
valueBox(
value = paste(format(max(nz$per_million), big.mark = ","), "", sep = " "),
caption = "Cases per million",
icon = "fas fa-user-md",
color = "silver"
)
```
### update {.value-box}
```{r}
valueBox(
value = paste0(format(Sys.time(), "%B %d"),", ",format(Sys.time(), "%H:%M")),
caption = "Updated",
icon = "fas fa-clock",
color = "silver"
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (New Zealand only)
```{r}
# plot nz growth
#who <- "New Zealand"
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p1 <- covid19 %>% filter(country %in% who, per_million > 5) %>%
group_by(country) %>%
mutate(days5 = dense_rank(date)) %>%
ungroup() %>%
#ggplot(aes(x = date, y =cum_cases , colour = country)) +
ggplot(aes(x = days5, y = per_million, colour = country)) +
geom_point() +
geom_line()
p1 + theme_wsj() + scale_colour_wsj()
```
**Comparison**
=======================================================================
Column {data-width=400}
-------------------------------------
### **Daily new cases**
```{r}
#----------------------------------------
# Plotting the data
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p2 <- covid19 %>% filter(country %in% who , cum_cases > 0) %>%
group_by(country) %>%
mutate(days = dense_rank(date)) %>%
ungroup() %>%
ggplot(aes(x = days, y = cum_cases, colour = country)) +
geom_point() +
geom_line() +
scale_y_continuous(trans = "log10",
breaks = trans_breaks("log10",function(x) 10^x), labels = trans_format("log10", math_format(10^.x)))
p2 + theme_wsj(color = "gray") + scale_colour_wsj("colors6")
```
### **Daily Cases**
```{r}
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p3 <- covid19 %>% filter(cum_cases > 360) %>%
arrange(cum_cases) %>%
ggplot(aes(x = date, y = country, fill = cases)) +
geom_tile(colour = "lightgray") +
scale_fill_gradient(low = "orange", high = "orangered4")
p3 + theme_wsj(color = "gray", ) + scale_colour_wsj() +
theme(axis.text.y = element_text(size = 5))
```
**Map**
=======================================================================
### **World map of cases**
Map Coming Soon
```{r}
# make a map
#library(leaflet)
#library(leafpop)
#map_object %>%
# addLayersControl(
# overlayGroups = names(cv_data_for_plot.split),
# options = layersControlOptions(collapsed = FALSE)
# )
```
**About**
=======================================================================
**The Coronavirus Dashboard: the case of New Zealand**
This Coronavirus dashboard: the case of New Zealand provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for New Zealand. This dashboard is built with R using the R Makrdown framework and with Flexadashboard.
**Code**
The code behind this dashboard is available on [GitHub](https://github.com/mattbixley/covid19){target="_blank"}.
**Data**
The input data for this dashboard is the dataset available from the
[{ width=3%}](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide){target="_blank"} European Centre for Disease Prevention and Control
The data and dashboard are refreshed on a daily basis.
Some data is also pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/CSSEGISandData/COVID-19){target="_blank"}.
But I find them late to update due to time zone differences.
**Contact**
For any question or feedback, you can email me or report an issue at the [GitHub repo](https://github.com/MattBixley/COVID19/issues){target="_blank"}
**Update**
The data is as of `r format(max(covid19$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")` at `r format(Sys.time(), "%H:%M")`.
European Centre for Disease Prevention and Control